Sensitivity to Constraint.
We test the DA performance of IBAN / SIBAN in term of mIoU with varying \(I_c\)  over a range {100, 200, 300, 400, 500}, where a smaller \(I_c\)  indicating a more strict information constraint on the latent features. Fig. REF  presents the test results, in which we can see that SIBAN outperforms IBAN in all constraint cases. For SIBAN, the appropriate choice of \(I_c\)  is between 200 and 400. An \(I_C\)  with too small value would eliminate too much essential information, while an excessively large \(I_c\)  would degrade SIBAN to the baseline model since it introduces too much noise. We can also observe that the IBAN is more sensitive to the constraint. When using \(I_c = 300\) , both IBAN and SIBAN surpass the feature-space baseline significantly and SIBAN can even outperform the state-of-the-art segmentation-space DA methods [1]}. From the result, we can conclude that our proposed SIBAN has bridged the performance gap between feature-space and segmentation-space DA methods [1]}.
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